Apparatus and method for efficient representation of periodic and nearly periodic signals for analysis

ABSTRACT

Apparatus for graphically representing a substantially periodic signal having substantially periodic segments, comprising a graphic transformer for converting the periodic segments into encodings respectively for arrangement in a first dimension and successively indexing the encodings to form a two-dimensional coded image, thereby to represent, within the image, dynamic changes of the periodic segments over the signal.

RELATED PATENT APPLICATION

This application is a National Phase Application of PCT/IL02/00222International Filing Date 19 Mar. 2002, which claims priority from U.S.Provisional Patent Application Ser. No. 60/276,481 filed 19 Mar. 2001.

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to an apparatus and method for efficientrepresentation of periodic and nearly periodic signals for analysis.

Analysis of periodic or nearly periodic signals does not, as a rule,rely solely on an inspection of single periods themselves. In manycases, inspection of an evolution of changes in a periodic part of asignal over time may reveal important information. However, inrelatively long signals (containing a large amount of periods) astandard continuous representation of the signal makes inspection of thesignal (i.e. the entire data) a tedious and ineffective task, especiallyif signal changes are gradual and moderate. One specific example of thisproblem is found in electrocardiogram (ECG) analysis of the human heart.

An ECG describes the electrical activity of the heart's muscle, whichinitiates and accompanies its mechanical activity. An ECG signal isrecorded by body surface electrodes that measure the change inelectrical potentials over the body due to the propagating electricalactivation in the heart. Visual inspection of the ECG signal is thebasic and most common non-invasive means for detection and diagnosis ofcardiac abnormalities. The main features forming the basis for ECGdiagnosis, which give indication of gross morphological changes, are:the P wave, describing the depolarization of the atria; the QRS complex,describing ventricles depolarization; and the T wave, describingventricle repolarization.

Thus, extraction of information related to heart activity by means ofECG inspection and analysis concentrates on what is known as the P-QRS-Tsegment of the signal. However, analysis typically ignores largeportions of the ECG signal—those portions corresponding to periodsbetween any two consecutive heart beats. With the exception of theidentification and interpretation of cardiac arrhythmias, most of thecommonly used diagnostic aids based on ECG data, such as an S-T segmentshift, prolonged and bizarre QRS complex patterns, or T wave inversionare—as indicated by their names—related primarily to inspection of theP-QRS-T segment of the signal.

The significant frequency range of ECG signals is traditionallyconsidered to be from 0.05–100 Hz. Although all common diagnosticmethods mentioned above are based solely on information contained in the0.05–100 HZ frequency range, valuable information is known to be foundin higher frequencies. Abboud et al (“High-Frequency ElectrocardiogramAnalysis of the Entire QRS in the Diagnosis and Assessment of CoronaryArtery Disease”, Progress in Cardiovascular Diseases, Vol. XXXV, No. 5,March/April 1993), the contents of which are hereby incorporated byreference, have shown in a series of studies a correlation betweensignificant decrease in the high frequency (HF), namely 150–250 Hz,content of the QRS signal and an ischemic condition of the heart.

In both traditional ECG based diagnosis methods and in more recent HFECG based methods, it is common that cardiac abnormalities (ischemiabeing the most important) which are not present at rest, may bemanifested during physiological stress, usually caused by exercise.Thus, comparison of an ECG signal of a subject under physiologicalstress with the same subject's ECG signals at rest and during a recoveryperiod is commonly used for detection and identification of cardiacabnormalities. It should be noted, however, that existing continuousrepresentations of the ECG signal do not allow easy inspection of theevolution of a signal during a test—the test being typically 10–20minutes long, thus involving many hundreds of heart beats.

While standard ECG diagnosis methods may be (and actually are) based onlocal data, the situation in HF ECG methods is a more delicate one: assignal to noise ratio is far worse in the HF range than in the standard0.05–100 HZ range, accuracy of local data in the HF portion of a signalmight not be sufficient. Therefore, whereas a global representation of astandard ECG signal of a complete exercise test may be viewed as adiagnostic aid, serving as an improvement upon traditional ECG analysismethods, it is of utmost importance in the diagnosis and interpretationof HF ECG signals.

Further studies by Beker et al (“Analysis of High Frequency QRSPotential during Exercise Testing Patients with Coronary Artery Diseaseand in Healthy Subjects”, Biomedical Engineering Department, Faculty ofEngineering, Tel-Aviv University, 1995), the contents of which arehereby incorporated by reference, showed that a decrease of the HF ECGof a QRS complex during exercise test could serve as an indication forearly detection of ischemic pathologies. These findings make inspectionof an HF ECG highly interesting, and call for development of tools thatmay tackle problems such an inspection imposes. An HF ECG signal has asignificantly lower amplitude than a standard ECG, and therefore an HFECG cannot be usefully dealt with, without first improving its signal tonoise ratio.

The results presented by Beker et al. are based on a comparison of theHF ECG signal at rest with the same signal during exercise. However, theHF ECG signal is susceptible to drastic and sudden changes that are notnecessarily due to an ischemic condition of the heart, but rather toouter changes such as the patient's body position. Any diagnostic toolbased on HF ECG will have to differentiate between these sudden changesand the changes in the signal caused by ischemia.

It is clear from the discussion above that a local inspection of the HFECG signal is prone to errors for the following reasons:

-   -   It seems impossible to reduce the level of noise to a completely        insignificant one, without distorting the HF ECG signal itself.        Thus some level of noise will always occur in the signal, and        its overall effect should be evaluated over a relatively long        timescale.    -   It seem impossible to determine, by inspecting the HF level of        two different QRS complexes, whether the difference between        these signals is due to ischemia or to an artifact caused by a        movement of the patient.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention there is thusprovided an apparatus for graphically representing a substantiallyperiodic signal having substantially periodic segments, the apparatuscomprising a graphic transformer for converting said periodic segmentsinto encodings respectively for arrangement in a first dimension andsuccessively indexing said encodings to form a two-dimensional codedimage, thereby to represent, within said image, dynamic changes of saidperiodic segments over said signal.

Preferably, a period identifier, associated with said graphictransformer, identifies a period of said signal, thereby to enablesegmentation of said signal into said periodic segments.

Preferably said period identifier comprises a time-oriented aligner anda morphology-oriented aligner to allow substantial alignment of saidperiodic segments.

Preferably said time oriented aligner is operable to effect saidalignment by defining a reference point in each of a plurality of saidperiodic segments.

Preferably said morphology oriented aligner is operable to effect saidalignment against a segment template.

Preferably said segment template is at least one chosen from a listcomprising: a set of local maxima and minima of said periodic segment, acharacteristic overall shape of said periodic segment, and acharacteristic portion of said periodic segment.

Preferably said time-orientated aligner and said morphology-orientedaligner are operable together to effect alignment of said periodicsegments.

Preferably said time-orientated aligner is operable to effect alignmentof said periodic segments.

Preferably said morphology-orientated aligner is operable to effectalignment of said periodic segments.

Preferably said periodic segment comprises a complex of features andsaid graphic transformer is associated with a feature encoder forencoding a feature of said periodic segment into said encoding.

Preferably said encoding comprises a color scale used against a relativeposition within said period, thereby to form said encoding as acontinuous colored line representing said feature at respectivepositions of said periodic segment.

Preferably said encoding comprises a gray scale used against a relativeposition within said period, thereby to form said encoding as acontinuous gray scale line representing said at least one feature atrespective positions of said period.

Preferably said encoding comprises a gray scale used against a relativeposition within said period, thereby to form said encoding as acontinuous gray scale contour map.

Preferably said graphic transformer further comprises a segment smootheroperable in association with said feature encoder to effect smoothing ofdata within said periodic segment.

Preferably said feature is amplitude.

Preferably said segment smoother comprises an interpolation filter and asignal data reducer.

Preferably said interpolation filter is operable to selectivelyinterpolate data of regions of respective periodic segments to enable asmooth representation of said data.

Preferably said signal data reducer is operable to selectively reducedata of regions of respective periodic segments to enable a smoothrepresentation of said data.

Preferably said signal is a biological signal.

Preferably said signal is an ECG signal.

Preferably said ECG signal is at least one of a group comprising anexercise ECG test signal and a rest ECG signal.

Preferably said ECG signal is a Holter recording signal.

Preferably said period identifier is operable to use selected frequencyband components to carry out said alignment.

Preferably said period identifier is further operable to use saidselected frequency band components to improve said alignment.

Preferably said period identifier further comprises:

-   -   a noise reducer operable to reduce noise in said aligned        periodic segments,    -   a filter for selected frequency components, said filter operable        upon said aligned periodic segments following noise reduction,        and    -   a matrix orderer operable to order said aligned periodic        segments in chronological order, following said frequency        filtering.

Preferably said signal is an ECG signal.

Preferably said period identifier further comprises:

-   -   a period averager, usable in association with said time-oriented        aligner and said morphology-oriented aligner to provide        averaging over said period segments,    -   a template filter for filtering over said period segments, and    -   an array filter for filtering over an array of segments, to        further improve said alignment.

Preferably said period averager is operable to average respectiveperiods by averaging a predetermined number of consecutive periods insaid array of periodic segments having a substantially low level ofnoise in a frequency band of interest to create a lower noise array ofperiodic segments.

Preferably said template filter is operable in association with saidperiod averager, so that said lower noise array of periodic segments isselected frequency filterable to create a template array of periodicsegments.

Preferably said array filter is operable in association with saidtime-oriented aligner and said morphology-oriented aligner by selectedfrequency filtering said array of periodic segments to create a filteredarray of periodic segments.

Preferably said filtered array of period segments is realignable usingsaid template array of periodic segments as a segment template.

According to a second aspect of the present invention there is thusprovided an electronic graphic representation of a periodic orsemi-periodic signal, comprising:

-   -   a first dimension representing properties for a periodic segment        of said signal, and    -   a second dimension representing successive segments, thereby to        form a two-dimensional coded image representative of dynamic        changes within the periodicity of said signal.

Preferably said properties comprise a feature of said periodic segment.

Preferably comprising visual encoding of said feature.

Preferably comprising visual encoding of derived properties of saidfeature.

Preferably said visual encoding comprises a gray scale.

Preferably said visual encoding comprises a color scale.

Preferably said periodic signal is a biological signal.

Preferably said periodic signal is an ECG signal.

According to a third aspect of the present invention there is provided amethod of representing a substantially periodic signal havingsubstantially periodic segments to show dynamic changes in theperiodicity of a signal, the method comprising transforming saidperiodic segments into encodings respectively for arrangement in a firstdimension and successively indexing said encodings to form atwo-dimensional coded image, thereby to represent, within said image,dynamic changes of said periodic segments over said signal.

Preferably the method further comprises identifying a period of saidsignal, thereby to enable segmentation of said signal into said periodicsegments.

Preferably identification of said period of said signal furthercomprises the steps of:

-   -   aligning said periodic segments according to at least-one chosen        from a list of time orientation and morphology orientation; and    -   reducing noise in said aligned periodic segments;

Preferably said period identification is performed using selectedfrequency band components to carry out said alignment.

Preferably said period identification further comprises the steps of:

-   -   filtering a selected frequency band from said noise-reduced        aligned periodic segments; and    -   ordering said filtered periodic segments in a matrix in        chronological order.

Preferably said period identification uses said selected frequency bandcomponents to improve said aligning of said periodic segments.

Preferably said signal is a biological signal.

Preferably said signal is an ECG signal.

Preferably said ECG signal is obtained during at least one chosen from alist comprising exercise ECG tests and rest ECG tests.

Preferably signal ECG is obtained during a Holter recording.

Preferably at least one of a list comprising identifying andtransforming, takes place in a personal digital assistant-like device.

Preferably at least one of a list comprising identifying andtransforming, takes place in a portable ECG device.

Preferably further comprising manipulating said two-dimensional imagegraphically.

Preferably said manipulation comprises at least one chosen from a listcomprising zoom in and zoom out.

Preferably said two-dimensional coded image is decodable to yield arepresentation of said segmented periodic segments.

Preferably said substantially periodic signal comprises a set of signalscollected from at least two non-continuous signal portions.

Preferably said set of signals comprises a plurality of records spanninga period of an order of magnitude within a range of seconds to years,and more specifically in the ranges of tens of minutes to tens of hours.

Preferably said signal is a record of a biological signal comprising atleast one chosen from a list comprising: EEG signals and ECG signals,wherein said signal is correlated to a series of repetitive externalstimuli.

According to a fourth aspect of the present invention there is thusprovided a method of representing a non-periodic signal containing aplurality of recurring signal segments to show dynamic changes in saidsignal segments, the method comprising transforming said signal segmentsinto encodings respectively for arrangement in a first dimension andsuccessively indexing said encodings to form a two-dimensional codedimage, thereby to represent, within said image, dynamic changes of saidsignal segments over said signal.

Preferably the method comprises identifying said signal segments.Alternatively it may work on previously identified signal segments.

BRIEF DESCRIPTION OF THE DRAWINGS

The file of this patent contains at least one drawing executed in colorphotograph. Copies of this patent with color photograh(s) will beprovided by the Patent and Trademark Office upon request and payment ofnecessary fee.

For a better understanding of the invention and to show how the same maybe carried into effect, reference will now be made, purely by way ofexample, to the accompanying drawings.

With specific reference now to the drawings in detail, it is stressedthat the particulars shown are by way of example and for purposes ofillustrative discussion of the preferred embodiments of the presentinvention only, and are presented in the cause of providing what isbelieved to be the most useful and readily understood description of theprinciples and conceptual aspects of the invention. In this regard, noattempt is made to show structural details of the invention in moredetail than is necessary for a fundamental understanding of theinvention, the description taken with the drawings making apparent tothose skilled in the art how the several forms of the invention may beembodied in practice. In the accompanying drawings:

FIG. 1 is a simplified block diagram indicating the salient elements ofan apparatus for efficient representation of periodic and nearlyperiodic signals for analysis, in accordance with a first preferredembodiment of the present invention;

FIG. 2 is a schematic diagram of one typical QRS complex in an ECGsignal;

FIG. 3 is a graphical representation of a two-dimensional matrix,corresponding to electrical potential levels of QRS complexes of apatient during an 18-minute exercise test, in accordance with a secondpreferred embodiment of the present invention;

FIG. 4 is a simplified flowchart summarizing a method for efficientrepresentation of an HF ECG signal in accordance with a third preferredembodiment of the current invention;

FIG. 5 is a simplified flowchart of an alternate method to obviate finealignment in efficient representation of an HF ECG signal in accordancewith a fourth preferred embodiment of the current invention; and

FIG. 6 is a schematic representation of a 2-D presentation of a HF QRSsignal during a complete exercise test, accompanied by HF QRS signals atvarious stages of exercise, both according to the preferred embodimentsas described in FIG. 4 and FIG. 5.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Preferred embodiments of the present invention comprise an apparatus andmethod for a two-dimensional representation of transformedone-dimensional periodic and nearly periodic signals. Transformation ofthe periodic part of the initial signal over time is indicated as one ofthe axes of the two-dimensional representation. Preferred embodimentsare applicable to any signal in which most of the relevant informationis concentrated in a distinct set of relatively short time-intervalscontaining morphologically close signal segments.

The embodiments are preferably applied, but not exclusively, tobiological periodic signals as exemplified by human ECG signals.Preferred embodiments of the present invention include:

-   -   a. Identification and extraction of all occurrences of the        periodic part of a signal to be analyzed, thereby creating a set        of periodic segments.    -   b. Alignment of the set of periodic segments to allow        comparisons across selected parts of each of the set of periodic        segments.    -   c. Representation of the aligned set of periodic segments in a        two dimensional format to enable and enhance analysis.

Further preferred embodiments of the present invention include anapparatus and method for representation of both standard and HighFrequency (HF) ECG signals, providing a global view of the significantinformation in an ECG signal of many hundreds of heartbeats at a time.The preferred embodiments provide a tool for analysis of an ECG signaland, more particularly, a means to detect transient changes in a P-QRS-Tsegment during exercise or any other time dependent test.

Before explaining the embodiments of the invention in detail, it is tobe understood that the invention is not limited in its application tothe details of construction and the arrangement of the components setforth in the following description or illustrated in the drawings. Theinvention is applicable to other embodiments or of being practiced orcarried out in various ways. Also, it is to be understood that thephraseology and terminology employed herein is for the purpose ofdescription and should not be regarded as limiting.

Reference is now made to FIG. 1 which is a simplified block diagramindicating the salient elements of an apparatus for efficientrepresentation of periodic and nearly periodic signals for analysis, inaccordance with a first preferred embodiment of the present invention.In the present discussion, “signal” is used to refer to the entire datacollected, and “segment” is used to indicate an arbitrary part of thesignal (usually, but not necessarily, a continuous part of the signal).The apparatus described in FIG. 1 may be applied to any signal withperiodic or nearly periodic behavior, enabling a 2-dimensionalrepresentation of 1-dimensional signals, adding to the presentationinformation related to the evolution of changes in the periodic part ofthe signal with time.

A signal collector 110 provides input to the system. This element isfurther discussed below. The signal is then transferred to a periodidentifier 120 which operates on a given segment of the signal, which istypically the entire signal but may be a portion of it, and definesperiods for the signal segment. As a continuation of processing withinperiod identifier 120, periods are then aligned with one another basedon one or more criteria further described below. Aligned periods, alsoreferred to as aligned events, are then transferred to a graphictransformer 130 where, according to one or more criteria, the periodsare transformed into a 2-D presentation, so that periods are representedon one axis of a 2-D presentation with the second axis representing thesuccession of periods.

A particular example of a signal with periodic or nearly periodicbehavior is an electrocardiogram (ECG). In this case, signal collection110 is performed by body surface electrodes that measure a change inelectrical potentials over a patient's body due to propagatingelectrical activation in the heart, for example, during an exercisetest. Identification of periodicity 120 (or creation of a set of events)depends highly on the signal to be investigated and on the type ofanalysis it will be subject to. For instance, detection of prolonged QRScomplexes in an ECG signal during exercise can be done using well-knownalgorithms to first detect and extract all the QRS complexes in thesignal and then to identify the onset and the offset of each of the QRScomplexes.

While identification of periodicity 120 in biological signals is acommon and largely practiced procedure, aligning a set of events is amuch less familiar procedure that plays a key role in ordering data toenable a comparative two-dimensional representation. Only a robustalignment algorithm can assure a meaningful comparison between twodistant segments of a biological (and characteristically relativelyunstable) signal. Alignment algorithms depend on parameters of aninspected signal. For that reason it is difficult to imagine a generalalignment procedure that may cope with any given set of events.Furthermore, it should be stressed that, in general, alignment of a setof events would not automatically emerge as a by-product of the creationof that set. This point is true not only for delicate signals, such asHF ECG, which will be discussed below, but also for more robust signals,such as a standard ECG.

Reference is now made to FIG. 2 which is a schematic diagram of onetypical QRS complex in an ECG signal. A horizontal axis 210 representstime, in units of 1 msec. A vertical axis 220 represents amplitude inmV. A shape of a QRS wave is typified by various points such as: QRSonset 230; R-wave peak 240; and QRS offset 250. Those skilled in the artwill note that FIG. 2 represents one of many such complexes present in asignal collected from an ECG, and that the exact shape of such a complexmay vary significantly. The QRS complex shown in FIG. 2 is referred tobelow and in further discussion.

Regardless of a specific implementation of an alignment algorithm, twodifferent approaches to the alignment problem are considered:

-   -   time oriented alignment: detecting a single reference point in        each event, for example, onset 230 or offset 250 points of each        event, and then aligning all events according to that set of        points (for example, fixing a zero point of each event at a        chosen reference point); and    -   morphology oriented alignment: for all events, find an alignment        that best approximates a given template, for example, min RMS of        a difference signal or max cross-correlation. Obviously, the        morphology oriented alignment method does not have to take into        account all existing data for each event, and the morphology        oriented alignment method may be effected using any partial        information, such as a set of local maxima and minima of each        event.

Clearly, the choice of an alignment method depends mostly on the signaland data to be extracted. In many cases a combination of the twomethods, such as a time oriented first approximation, refined by amorphologically oriented alignment, will be needed.

A major aspect of the graphic transformer 140 of FIG. 1 is itspresentation of an aligned set of events as a two-dimensional matrix,where every column corresponds to a single event, or to a time-averagedgrouping of events. (The following discussion is equally valid forconsidering every row as a single event, in which case, the nomenclaturefor horizontal and vertical noted below would be reversed.) Thus, bothdirections of the matrix may be seen as representing a time variable.The vertical direction represents a shorter time interval correspondingto a duration of a single event (time-averaged grouping of events). Aspecific example of a single event would be one heartbeat period,typically on the order of under one second. In the case of atime-averaged grouping of events, each grouping could be on the order of10 seconds. Note that the actual length of each such event also dependson the signal sampling rate. The horizontal axis represents a largertime interval corresponding to a length of the whole signal. The actuallength of the segment depends only on the size of the set of events. Aspecific example of a length of the segment would be on the order of 20minutes—a typical time for a stress test.

Alignment of the set of events allows viewing each row of the matrix asa set of corresponding points in an array of recurring events. Changesalong a row of the matrix correspond to changes in recurring events.Graphical representation of the matrix therefore is a useful tool foremphasizing and efficiently detecting changes in recurring events.

To simplify notation henceforth, it is assumed that each column of thematrix represents a single period and that the rows of the matrixcorrespond to the evolution of the period with time. As previouslymentioned, this assumption does not in any way constrain the alignmentmethod and subsequent algorithms.

Reference is now made to FIG. 3 which is a graphical representation of atwo-dimensional matrix, corresponding to electrical potential levels ofQRS complexes of a patient during an 18-minute exercise test, inaccordance with a second preferred embodiment of the present invention.The graphical representation is based on a grayscale, color scale, orany other visual scale varying over the whole range of values in thematrix. The events corresponding to each column of the matrix, i.e. eachvertical column in the graphical representation of FIG. 3, aremorphologically aligned QRS complexes (such as shown previously in FIG.2) extracted from a patient's standard ECG.

The axes in the current figure represent; time along exercise test 310(10 second units), time along QRS complex 320 (msec), and amplitude 330(mV). The amplitude axis 330 is further indicated as a color-codedscale. In the color scale of the present figure, the QRS complex itselfcan be easily detected between the two thin cyan strips 340corresponding to the onset and to the offset of the QRS at about rows 45and 125 respectively, while the red strip 350 in the middle of thefigure represents a peak of the R-wave. The two large turquoise strips360 at the beginning and at the end of each column represent the end ofthe P-R and the beginning of the S-T intervals, respectively.

Analysis of the two-dimensional graphical representation immediatelyreveals the following observations:

-   -   A significant decrease of the QRS complex amplitude during        exercise, represented by a transition from the dark red color        corresponding to R-wave peak 350 in the beginning of the test to        an orange-yellow shade 355 at the time of peak exercise;    -   A significant prolongation of the QRS complex during exercise        corresponds to the growing distance between the two        above-mentioned cyan strips 340; and    -   A block in the conductive system of the heart at peak exercise        is a probable cause of the large and deep notch, corresponding        to the yellow-green-orange pattern 370 seen between x-axis        points 40 and 70.

As previously noted, inspection of HF ECG calls for solution of problemsposed due to the fact that an HF ECG signal is significantly weaker thana standard ECG and that an HF ECG signal is susceptible to drastic andsudden changes, not necessarily due to an ischemic condition of theheart. Providing a global overview of the evolution of an HF ECG duringa complete exercise test solves these problems:

-   -   as noise is assumed not to correlate with an HF ECG signal, its        effect is likely to change from one heart beat to another.        Viewing a large number of beats at a glance helps to sort out an        undistorted pattern of the HF ECG and to eliminate an effect of        noise; and    -   drastic sudden changes will differ from continuous gradual ones        in any global representation of the set of events, thus pointing        out the correct reference signal and sorting out artificial        changes not related to the heart, as noted above.

Reference is now made to FIG. 4 which is a simplified flowchartsummarizing a method for efficient representation of an HF ECG signal inaccordance with a third preferred embodiment of the current invention.Before describing the overall structure of the current figure, it isnoted that none of the stages of the current figure need to deal withany significant amount of raw data at a time. Therefore, the currentflowchart should be regarded as including any additional means for datastorage for each stage to enable both on line and off lineimplementation.

Signal acquisition 410 may be preferably done by placing electrodes onan individual's skin, thus acquiring a standard ECG signal. Signaldigitization 420 is accomplished by preferably digitizing the acquiredsignal with any A/D converter with an appropriate sampling rate (i.e.greater than twice the highest frequency of the signal to be analyzed).Note that signal digitization 420 typically also includes signalamplification prior to digitization. This step is followed by detectionof a QRS segment of the signal 430. Any standard QRS detection algorithmmay be used, however, attention must be given to the signal samplingrate and the frequency range being analyzed, as further discussed below.The method for QRS detection 430 provides an output which serves as afirst approximation for aligning QRS 440. QRS alignment is more finelytuned by an alignment algorithm, to be discussed below.

Note that alignment of QRS 440 appears in the flowchart before filter HF455; meaning alignment is performed over low frequency QRS complexes.This is due to the fact that, in general, the signal to noise ratio inthe HF band obviates effective signal processing. (Note that since QRSis considered to highly correlate with HF QRS, alignment of a lowfrequency signal will directly yield alignment of an HF signal.)

Noise reduction 450, following align QRS 440, is applied to increase thesignal to noise ratio. Then, filtering of the HF signal 455 follows anda fine alignment 460 is performed. Considerations for alignment 440,noise reduction 450, HF filtering 455, and fine alignment, all of whichmay be interrelated, are further described below. At this point, anarray of digitized waves, which has been filtered to the frequency bandof interest and aligned to an adequate value, is ordered into a matrixof events 465. A color scale is chosen 470, to reflect desired relativeemphases of amplitude values of the respective QRS complexes in thecontext of the representation of the matrix. The color scale is applied,and a 2-D presentation of the matrix 480 is produced.

Note that in QRS detection 430 any standard algorithm may be used. Onecommonly used algorithm consists of creating a template QRS complex (forexample, by manually selecting one of the complexes in the raw data),cross correlating it with the digitized signal, and obtaining a crosscorrelation function. Local maxima of that function having a valueexceeding a predetermined threshold may correspond to QRS complexes ofan ECG signal (provided the threshold used is high enough). Another wellknown algorithm consists of locating all local maxima (or minima,depending on the polarization of the signal) of the overall signal thathave a value higher or lower than a predetermined value corresponding toan expected level of the QRS amplitude.

Alignment of QRS 440 complexes obtained will depend highly upon: asampling rate of the A/D converter, which determines basic time units inwhich the data is given (e.g. a basic time unit of 1 ms corresponds to asampling rate 1 KHz); frequency-range of the signal to be analyzeddetermining the required accuracy level of the alignment algorithm (e.g.an inaccuracy level of 1 ms will not suffice for the alignment ofsignals of 250 Hz as it corresponds to a 90° phase shift at thatfrequency); and the method for QRS detection 430 whose output provides afirst approximation for the alignment algorithm.

It is noted that detection algorithms might, at best, be used to align aset of events to an accuracy level on the order of magnitude of thebasic time unit determined by the A/D converter. It may be assumed inmost cases that a sub-sampling accuracy will be needed. Thus in general,the results of the QRS detection 430 algorithm are used as a firstapproximation. Results are subsequently more finely tuned by the QRSalignment 440 algorithm.

Noise reduction 450 is one of the most widely used procedures in signalprocessing, ranging over a large number of algorithms and techniques. Anoise reduction process commonly used in ECG analysis is that ofaveraging a number, n, of ECG cycles over a time period resulting in anaveraged signal with a noise level reduced by factor √n, relative to thelevel of the original signal. It should be noted, however, that thisprocedure tends to attenuate transient changes appearing in a number ofECG cycles of the same order as n (the number of averaged cycles). Thus,when using an averaging process, some preliminary assumption should bemade concerning the expected duration of significant changes sought inthe current analysis.

Design of filter HF 455 for the processed, noise reduced standard ECGQRS complexes obtained from the previous stages preferably takes intoaccount several filter parameters including: the locality of thephenomenon in the time domain (i.e.: in looking for a given phenomenonin the QRS complex—either in a standard ECG or a HF ECG—what is thecoarsest time resolution in which the given phenomenon may still bedetected.); and the locality of the phenomenon in the spectrum domain(i.e. some of the most important phenomena occurring in an HF ECG do nothave any trace in a standard ECG.) As the energy levels of the ECGsignal in the lower band of the spectrum are much higher, anotherconsideration is what is the largest frequency range in which a lowfrequency signal will not cover the above-mentioned phenomena.

Because the above-mentioned filters parameters are sometimes mutuallyexclusive, an optimal filter will usually depend heavily on the traitsof the phenomenon to be dealt with. It should be stressed that differentimplementations of the algorithm, based upon a user's preference inperforming analysis, may call for different filters and differentfrequency bands to be analyzed. These parameters should, of course,relate to the data that is to be extracted from the signal.

Note that while both noise reduction 450 and filtering HF 455 take intoaccount time related considerations, the time related phenomenaappearing at each of these steps are not interrelated, and that theyrelate to different time axes of the 2-D presentation 480. Indeed, whilethe locality of the phenomenon in the time domain—discussed in the lastparagraph—deals with the problem of locating a transient phenomenon inany given QRS complex, the expected duration of the change—noisereduction 450—deals with the question of the expected number of QRScomplexes to be affected by a given phenomenon.

In many cases no correlation can be found between changes in thestandard ECG and the HF ECG, as mentioned above. (In fact, thesuperiority of the HF ECG signal over the standard ECG signal as adiagnostic aid is due to phenomena related to this point.) In some casesthis situation acts as a double-edged sword since the alignment processof QRS complexes is done over the standard ECG signal, and thisalignment process can be affected by local changes of the standard ECGsignal which don't necessarily affect the HF signal. For this reason itis apparent that in some cases an iterated application of an alignmentalgorithm over small neighborhoods about the reference points obtainedin align QRS 440, using HF QRS waves (and standard QRS complexes as donepreviously) may improve results of the first alignment.

In another approach, tackling discrepancies of an alignment of an LFsignal with that of an HF can be performed at an earlier stage, avoidingthe fine alignment 460, as described below.

Reference is now made to FIG. 5 which is a simplified flowchart of analternate method to obviate fine alignment in efficient representationof an HF ECG signal in accordance with a fourth preferred embodiment ofthe current invention. Steps that are the same as those in previousfigures are given the same reference numerals and are not describedagain except as necessary for an understanding of the presentembodiment.

Proceed with signal acquisition 410, signal digitization 420, QRSdetection 430, and align QRS 440 in similar fashion as described forFIG. 4, to create an array of aligned low frequency QRS complexes. Thisgroup of steps is initial LF QRS align 510 in the present figure. Usethe aligned periods to create an array of waves with a relatively lowlevel of noise in the frequency band of interest. This can be done asdescribed in reduce noise 450 in FIG. 4, or more simply by averaging apredetermined number of consecutive periods in the array with relativelylow level of noise in the frequency band of interest 520. Band-passfilter each of the periods previously obtained at a desired frequencyrange to create a set of reference/template HF periods 530. Band-passfilter each of the periods created from initial LF QRS align 510 at adesired frequency range to create an array of HF waves 540. Proceed withalign QRS 440.1, followed by reduce noise 450.1 (both as previouslydescribed in FIG. 4 as QRS 440 and reduce noise 450, respectively). Usethe present array of HF waves (obtained from create array of HF waves540) as input to align QRS 440.1 and with the array of averaged andband-passed waves (obtained from create a set of reference/template HFperiods 530) as a template for align QRS 440.1.

Restricting application of the alignment procedure in the present figureto one period at a time makes it possible to align periods withrelatively high S/N ratio. This is because each segment to be examinedactually contains a QRS complex and only one such complex. Furthermore,a cross-correlation function of the LF signals (calculated in initial LFQRS align 510) yields a good indication of where a reference point, suchas that used in the previous discussion of FIG. 2 regarding timeoriented alignment.

However, as the S/N ratio in the HF band is much lower than in thestandard ECG, it is possible that the S/N ratio in the HF band will notallow alignment in the HF band, even as described above—while alignmentmay be achieved in the standard ECG. Therefore, in order to implementthe modified algorithm described above, an assumption regarding the S/Nratio in the HF band, such as that previously noted regarding noise inthe HF versus LF band, should be made so that the alignment procedurecould be considered accurate enough.

Irrespective of which method is applied in the previous stages (i.e.according to the embodiments described in FIG. 4 or described in FIG. 5)it may now be assumed that an array of digitized periods filtered to thefrequency band of interest and aligned up to an adequate level has beenobtained. Sorting the array time-wise (i.e. so that periodscorresponding to earlier parts of the signal appear earlier in thearray, etc) and viewing it as a matrix, where each column represents asingle period, the period alignment now assures that each row in thematrix represents the evolution of the portion of the QRS signalcorresponding to that point over a whole time period. A graphicalrepresentation of the matrix provides a tool where changes in a signalrecorded over a relatively long time may be detected at a glance.

Returning to FIG. 4, the steps “choose color scale 470” and “2-Dpresentation of matrix 480” are applicable in exactly the same way notonly to the matrix of events in the step “create a matrix of events465”, but are applicable to any matrix of values derived from the matrixof events following mathematical manipulations (such as band-passfiltering, integration, differentiation etc.) of the columns. The step“choose color scale 470” depends heavily on signal parameters such asdynamic range and frequency of the components of the period (compared tothe sampling rate). In the case of HF ECG, frequency plays an importantrole in the choice of the color scale.

Frequency is also directly related to another preferred embodiment ofthe current invention, as described below. Because the sampling rate ofan A/D converter is typically 2–3 times that of the significantfrequencies of the HF ECG, the difference between two neighboringsamples (within the same period) may typically be on the order of theentire dynamic range of the signal amplitude. This means that a 2-Dpresentation of the HF signal without further processing, may result ina highly discontinuous picture with questionable value.

Two solutions to the problem noted above are:

-   -   1. increase each period's data (e.g. using interpolation        filters) to obtain a subsample interpolation up to a desired        level sufficient to ensure a smoother picture. This method is        actually applicable because the sampling rate is assumed to be        adequate, so that accurate interpolation is feasible, and        because increasing each period's data may be effected in a way        that assures that the data resolution increases while the        dynamic range of the signal does not change (e.g. using a        normalized interpolation filter); and    -   2. reduce the signal to a smoother signal, e.g. by presenting        the signal envelope, the signal absolute value envelope etc.

The two approaches noted above represent two mutually exclusivesolutions to the problem. The first solution adds redundant informationand thus reduces the rate of change from bit to bit. The second solutioneliminates possibly redundant information to achieve the same result ofperiod smoothing.

Reference is now made to FIG. 6 which is a schematic representation of a2-D presentation of a HF QRS signal during a complete exercise test,accompanied by six HF QRS signals at various stages of exercise, bothaccording to the preferred embodiments as described in FIG. 4 and FIG.5.

An envelope of the absolute value of a signal in the 2-D presentation ofFIG. 6 is represented as follows: Each line in the vertical axisrepresents a single QRS complex, whose upper part corresponds to theonset of the QRS and its lower part corresponds to the QRS offset. Thehotter color shades represent higher amplitudes. The evolution of thesix HF QRS signals a, b, c, d, e, and f, at various stages of exercise,may be perceived in the 2-D presentation above them. HF QRS signal “a”corresponds to the very beginning of the test, where one skilled in theart may easily detect a significant red strip preceded by an almostcontinuous yellow zone (vertically) which represents the approximatecontinuous increase in the signal's envelope. The drastic change inamplitude of the whole signal, as observed between images b and c aswell as between images e and f, corresponds to a disappearance andreappearance of the red strip. Further subtler changes in the signal(such as its division to two peaks or other features of the signal) maybe easily detected in the 2-D figure as well.

As specific application of the 2-D presentation method may be used foranalysis of data gathered during Holter recordings, where a patientcarries a small box that records his ECG continuously for 24 hours ormore and the recording is played back at a later time for analysis.Holter recordings are very long signals and their use calls for anefficient presentation and analysis system that may enable inspection ofthe data, so that no significant information is lost, at a reasonablespeed. A 2-D presentation of the signal as described in the preferredembodiments gives a user a means to inspect relatively long timeintervals in the signal at a glance, effectively reducing the timerequired for signal inspection and making the 2-D presentation usefulfor Holter recordings analyses—further described below. Moreover,features of the analysis system, which is applicable to Holter analysisas well as to other periodic or nearly periodic signals, make thepreferred embodiment even more useful when dealing with very longsignals.

A useful application of the preferred embodiments is their integrationinto personal digital assistant (PDA) or like devices containing ECGsignal acquisition, amplification, and digitization units. Due to theirrelatively small size and their graphical display, PDA-based ECG devicesare much better equipped for a 2-D presentation of the signal than forthe conventional 1-D method. Moreover, as is noted below, a 2-D signalrepresentation suggests a highly compressible data structure for storageand processing of an ECG signal. Effective data compression compatiblewith the present invention is of importance whenever storage space islimited (e.g. portable ECG devices) or whenever on-line data transfer isrequired (e.g. telemedicine).

In specific applications such as Holter recordings, analysis of verylong signals containing many thousands or even hundreds of thousands ofwaves may be required. In these cases, presentation of the whole matrixat a time may be too detailed to enable a initial global overview tolocate parts of the signal that demand closer attention. In many suchcases a smaller, representative matrix may be created from the originalhuge matrix, and the representative matrix may be analyzed using the 2-Dpresentation, as described above.

Creation of the representative matrix depends upon the phenomena beingviewed in the signal. For example, if the expected number of appearancesof a phenomenon in the wave is of interest, or if the phenomenon is nothighly transient, the original matrix may be diluted by presenting everyn^(th) column for a predetermined value n. Alternately, if thephenomenon is expected not to change significantly from one wave to thenext, averaging n columns to create a new representative column isappropriate. Finally, if a relatively short term or even transientchange in the wave over time is of interest, the original matrix may bediluted so that a (n+1) column in the representative matrix is the firstcolumn in the original matrix that differs sufficiently from the(already chosen) n column. In this case, if columns of the originalmatrix are indexed by the numbers 1 . . . n, choose a subsequence k₁ . .. k_(m) such that k_(j)<k_(j+1) for all j and such that for all j andall k_(j)<t<k_(j+1), the t column of the original matrix does not differsufficiently from the k_(j) column.

In reviewing the details of the preferred embodiment described above, itwill be apparent to one skilled in the art that the same methods can beapplied for the analysis not only of the HF components of the QRSsegment of the ECG but to any other portion of the signal (standard orHF).

In a preferred embodiment of the present invention the 2-D presentationof the signal will indeed serve as a diagnostic aid that, in cases wherestandard diagnostic aids are used, will help focus a user's attention tothose points where abnormalities do occur, and will further supply ameans to apply any other diagnostic tool for analysis of the phenomenaat these points. For example, a 2D representation of the evolution of anST segment of a signal during an exercise test will immediately revealany abnormalities (such as ST elevation or ST depression) as well as apositive assessment of the significance of the phenomenon (e.g. theactual depth of the depression). In order to examine the phenomenon moreclosely using standard tools a user may be supplied with an interface,with which he may specify a region of interest in the 2-D picture, forwhich a standard ECG representation may automatically appear.

Thus, in a preferred embodiment the user may be supplied with a,preferably graphic interface that will enable:

-   -   Manipulation of the 2-D picture; for example, when inspecting a        2-D representation of a complete heart cycle—i.e.        P-QRS-T-U-P—the QRS portion will usually dominate the color        scale. Therefore a change of the color scale may be needed to        inspect, for example, an ST segment more closely.

Other options may include:

-   -   change of scale of either axis of the picture;    -   addition/removal of markers, arrows, textual notes and other        aids;    -   addition/removal of grids of different affinities;    -   transition from a 2-color representation to a standard 3-D        representation;    -   addition/removal of indications of phenomena undetectable (or        not clear enough) in a 2-D representation (such as indications        of cardiac arrhythmias or exact QRS onset/offset); and    -   zooming in to view points or regions of interest in a 2-D        presentation in greater detail. These regions may correspond to        a specific time period (that is, a set of successive columns in        the matrix) or to a specific portion of the signal (such as the        peak of a QRS complex, corresponding to a set of successive rows        in the matrix) or to any combination of the above.

It should be stressed that both zooming in and the change of scaleoptions relate to two different processes described below.

The first process corresponds only to the (graphical) presentation ofthe matrix. Examples are: changing the color scale by adding colors;changing linear scaling with a logarithmic one; or simply zooming into asegment of the picture using standard image processing.

The second process corresponds to the analysis of very long signals, andto the manipulation of representative matrices as previously described.In theses cases, change of scale or zooming in operations may actuallyadd information contained in the original matrix, but not contained inthe representative matrix. Obviously, whenever the second process may beapplied all of the options of the first one may be available as well.

Further options may include:

-   -   direct access to standard diagnostic aids: transition to and        from a region specified by the user in the 2-D presentation to a        standard ECG signal presentation, these standard diagnostic aids        supplied with a complete toolbox for the analysis of standard        ECG signals. As noted above, the region of interest may        correspond either to a continuous segment in the standard 1-D        representation or to a set of discrete segments in the signal        (such as a set of successive ST segments, for example) or to any        combination thereof;    -   comparative aids, such as: evaluating changes in the 2-D        presentation of a series of tests acquired over relatively long        periods of time (hours, days, weeks and even years); evaluation        of a single period, or a set of periods, extracted from the 2-D        presentation against another signal corresponding to the same        point in time (for example, if the 2-D presentation showed a HF        QRS, a user may be able to compare a set of HF periods with the        corresponding standard ECG periods); and comparison of a set of        periods extracted from the 2-D presentation with the periods        themselves. The comparison may be done either by plotting the        periods one over the other in a single figure (accentuating the        differences between those periods) or by plotting the periods        each in a different figure (providing a more accurate overview        of each period).

More generally the 2-D presentation should be regarded as a new andconcise representation of periodic or almost periodic signals overrelatively long periods of time. The 2-D presentation is constructed ina way that relates each of its points to the area in the originalsignal, which it represents. Thus no data is lost and the user mayeasily and continually interchange the 2-D presentation of any part ofthe picture with the standard continuous representation of thoseportions of the signal it represents.

It should be noted that the matrix of periods constructed as a basis forthe 2-D presentation contains all (or at least, most) of the relevantinformation contained in the signal. Thus restricting analysis of thesignal to that matrix does not degrade analysis results. At the sametime, conciseness of the 2-D presentation may be very effective inapplications of preferred embodiments, such as the above mentioned PDAECG or other portable devices requiring transfer or storage of largequantities of periodic or almost periodic signals. Moreover, alignmentof the matrix of periods assures that a difference between any twoconsecutive periods is minimal, and therefore the matrix data may beeasily and effectively compressed (e.g. by keeping track only of thechanges from one period to the next) without any loss of information.

Moreover, the 2-D presentation discussed herein is applicable to anyperiodic and semi-periodic signal. Issues of noise reduction and choiceof frequencies to filter have been discussed above with respect to aspecific example of HF ECG. Those skilled in the art will see from theabove discussion how to apply these and similar issues to other types ofsignals.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable sub combination.

It will be appreciated by persons skilled in the art that the presentinvention is not limited to what has been particularly shown anddescribed hereinabove. Rather the scope of the present invention isdefined by the appended claims and includes both combinations and subcombinations of the various features described hereinabove as well asvariations and modifications thereof which would occur to personsskilled in the art upon reading the foregoing description.

1. Apparatus for graphically representing a substantially periodicsignal having substantially periodic segments, the apparatus comprising:a graphic encoder for converting said periodic segments into encodingsrespectively of properties thereof for arrangement in a single firstdimension, and an indexer, associated with said graphic encoder, forsuccessively indexing said encodings to show a succession of saidsegments over a second dimension, thereby to represent, within said twodimensions, dynamic changes of said properties of said periodic segmentsover said signal.
 2. Apparatus according to claim 1, further comprisinga period identifier, associated with said graphic transformer, foridentifying a period of said signal, thereby to enable segmentation ofsaid signal into said periodic segments.
 3. Apparatus according to claim2, wherein said period identifier comprises a time-oriented aligner anda morphology-oriented aligner to allow substantial alignment of saidperiodic segments.
 4. Apparatus according to claim 3, wherein said timeoriented aligner is operable to effect said alignment by defining areference point in each of a plurality of said periodic segments. 5.Apparatus according to claim 3, wherein said morphology oriented aligneris operable to effect said alignment against a segment template. 6.Apparatus according to claim 5, wherein said segment template is atleast one chosen from a list comprising: a set of local maxima andminima of said periodic segment, a characteristic overall shape of saidperiodic segment, and a characteristic portion of said periodic segment.7. Apparatus according to claim 3, wherein said time-orientated alignerand said morphology-oriented aligner are operable together to effectalignment of said periodic segments.
 8. Apparatus according to claim 3,wherein said time-orientated aligner is operable to effect alignment ofsaid periodic segments.
 9. Apparatus according to claim 3, wherein saidmorphology-orientated aligner is operable to effect alignment of saidperiodic segments.
 10. Apparatus according to claim 1, wherein saidperiodic segment comprises a complex of features and wherein saidgraphic transformer is associated with a feature encoder for encoding afeature of said periodic segment into said encoding.
 11. Apparatusaccording to claim 10, wherein said encoding comprises a color scaleused against a relative position within said period, thereby to formsaid encoding as a continuous colored line representing said feature atrespective positions of said periodic segment.
 12. Apparatus accordingto claim 10, wherein said encoding comprises a gray scale used against arelative position within said period, thereby to form said encoding as acontinuous gray scale line representing said at least one feature atrespective positions of said period.
 13. Apparatus according to claim10, wherein said encoding comprises a gray scale used against a relativeposition within said period, thereby to form said encoding as acontinuous gray scale contour map.
 14. Apparatus according to claim 10,wherein said graphic transformer further comprises a segment smootheroperable in association with said feature encoder to effect smoothing ofdata within said periodic segment.
 15. Apparatus according to claim 10,wherein said feature is amplitude.
 16. Apparatus according to claim 14,wherein said segment smoother comprises an interpolation filter and asignal data reducer.
 17. Apparatus according to claim 16, wherein saidinterpolation filter is operable to selectively interpolate data ofregions of respective periodic segments to enable a smoothrepresentation of said data.
 18. Apparatus according to claim 16,wherein said signal data reducer is operable to selectively reduce dataof regions of respective periodic segments to enable a smoothrepresentation of said data.
 19. Apparatus according to claim 1, whereinsaid signal is a biological signal.
 20. The apparatus of claim 19,wherein said signal is an ECG signal.
 21. The apparatus of claim 20,wherein said ECG signal is at least one of a group comprising anexercise ECG test signal and a rest ECG signal.
 22. The apparatus ofclaim 20, wherein said ECG signal is a Holter recording signal.
 23. Theapparatus of claim 3, wherein said period identifier is operable to useselected frequency band components to carry out said alignment.
 24. Theapparatus of claim 23, wherein said period identifier is furtheroperable to use said selected frequency band components to improve saidalignment.
 25. The apparatus of claim 24, wherein said period identifierfurther comprises: a noise reducer operable to reduce noise in saidaligned periodic segments, a filter for selected frequency components,said filter operable upon said aligned periodic segments following noisereduction, and a matrix orderer operable to order said aligned periodicsegments in chronological order, following said frequency filtering. 26.The apparatus of claim 23, wherein said signal is an ECG signal.
 27. Theapparatus of claim 24, wherein said period identifier further comprises:a period averager, usable in association with said time-oriented alignerand said morphology-oriented aligner to provide averaging over saidperiod segments, a template filter for filtering over said periodsegments, and an array filter for filtering over an array of segments,to further improve said alignment.
 28. The apparatus of claim 27,wherein said period averager is operable to average respective periodsby averaging a predetermined number of consecutive periods in said arrayof periodic segments having a substantially low level of noise in afrequency band of interest to create a lower noise array of periodicsegments.
 29. The apparatus of claim 28, wherein said template filter isoperable in association with said period averager, so that said lowernoise array of periodic segments is selected frequency filterable tocreate a template array of periodic segments.
 30. The apparatus of claim29, wherein said array filter is operable in association with saidtime-oriented aligner and said morphology-oriented aligner by selectedfrequency filtering said array of periodic segments to create a filteredarray of periodic segments.
 31. The apparatus of claim 30, wherein saidfiltered array of period segments is realignable using said templatearray of periodic segments as a segment template.
 32. An electronicgraphic representation of a periodic or semi-periodic signal,comprising: a. a first dimension carrying a one-dimensional encodingrepresenting properties for a periodic segment of said signal, and b. asecond dimension representing a continuation of said properties oversuccessive segments, thereby to form a two-dimensional coded imagerepresentative of dynamic changes within the periodicity of said signal.33. The representation of claim 32, wherein said properties comprise afeature of said periodic segment.
 34. The representation of claim 32,comprising visual encoding of said feature.
 35. The representation ofclaim 32, comprising visual encoding of derived properties of saidfeature.
 36. The representation of claim 32, wherein said visualencoding comprises a gray scale.
 37. The representation of claim 32,wherein said visual encoding comprises a color scale.
 38. Therepresentation of claim 32, wherein said periodic signal is a biologicalsignal.
 39. The representation of claim 38, wherein said periodic signalis an ECG signal.
 40. A method of representing a substantially periodicsignal having substantially periodic segments to show dynamic changes inthe periodicity of a signal, the method comprising: transforming saidperiodic segments into single dimensional encodings of propertiesthereof respectively for arrangement in a first dimension andsuccessively indexing said encodings to form a two-dimensional codedimage, thereby to represent, within said image, dynamic changes of saidperiodic segments over said signal.
 41. A method according to claim 40,further comprising identifying a period of said signal, thereby toenable segmentation of said signal into said periodic segments.
 42. Amethod according to claim 40, wherein identification of said period ofsaid signal further comprises the steps of: a. aligning said periodicsegments according to at least one chosen from a list of timeorientation and morphology orientation; and b. reducing noise in saidaligned periodic segments.
 43. A method according to claim 41, whereinsaid period identification is performed using selected frequency bandcomponents to carry out said alignment.
 44. A method according to claim43, wherein said period identification of said period further comprisesthe steps of: a. filtering a selected frequency band from saidnoise-reduced aligned periodic segments; and b. ordering said filteredperiodic segments in a matrix in chronological order.
 45. A methodaccording to claim 41, wherein said period identification uses saidselected frequency band components to improve said aligning of saidperiodic segments.
 46. A method according to claim 45, wherein saidsignal is a biological signal.
 47. A method according to claim 46,wherein said signal is an ECG signal.
 48. A method according to claim47, wherein said ECG signal is obtained during at least one chosen froma list comprising exercise ECG tests and rest ECG tests.
 49. A methodaccording to claim 47, wherein signal ECG is obtained during a Holterrecording.
 50. A method according to claim 40, wherein at least one of alist comprised of said identifying and said transforming, takes place ina personal digital assistant-like device.
 51. A method according toclaim 40, wherein at least one of a list comprised of said identifyingand said transforming, takes place in a portable ECG device.
 52. Amethod according to claim 40, further comprising manipulating saidtwo-dimensional image graphically.
 53. A method according to claim 52,wherein said manipulation comprises at least one chosen from a listcomprising zoom in and zoom out.
 54. A method according to claim 40,wherein said two-dimensional coded image is decodable to yield arepresentation of said segmented periodic segments.
 55. A methodaccording to claim 54, wherein said periodic segments are accessible bypointing on said two-dimensional image.
 56. A method according to claim40, wherein said substantially periodic signal comprises a set ofsignals collected from at least two non-continuous signal portions. 57.A method according to claim 55, wherein said set of signals comprises aplurality of records spanning a period of an order of magnitude within arange of seconds to years.
 58. A method according to claim 56, whereinsaid period is of an order of magnitude reaching hours.
 59. A methodaccording to claim 56, wherein said period is within a range of ordersof magnitude between hours and years.
 60. A method according to claim55, wherein said signal is a record of a biological signal comprising atleast one chosen from a list comprising: EEG signals and ECG signals,wherein said signal is correlated to a series of repetitive externalstimuli.
 61. A method of representing a non-periodic signal containing aplurality of recurring signal segments to show dynamic changes in saidsignal segments, the method comprising: transforming said signalsegments into single dimensional encodings of properties of saidsegments respectively for arrangement in a first dimension, andsuccessively indexing said encodings to form a two-dimensional codedimage, thereby to represent, within said image, dynamic changes of saidsignal segments over said signal.
 62. A method according to claim 60,further comprising identifying said signal segments.
 63. Apparatus foranalysis of a substantially periodic signal having substantiallyperiodic segments and long-term evolution of trends appearing over alarge number of said segments, the apparatus comprising: an encoder forconverting said periodic segments into encodings respectively ofproperties thereof, and an aligner for aligning said encodings such thatcorresponding properties of successive segments are aligned, therebyenabling extraction over said aligned segment encoding of parametersindicative of said long-term evolution of trends.
 64. Apparatusaccording to claim 63, wherein said substantially periodic signalcomprises high frequency information and wherein said aligner isconfigured to make use of properties derived at least partially fromsaid high frequency information.
 65. Apparatus according to claim 63,wherein said alignment comprises morphology oriented alignment. 66.Apparatus according to claim 63, wherein said alignment comprises timeoriented alignment.
 67. Apparatus according to claim 63, wherein saidalignment comprises morphology oriented alignment and time orientedalignment.